Balancing - Cancer from a primary care perspective. Diagnosis, posttraumatic stress, and end-of-life care.
Bibliographic record
Abstract
This thesis explores cancer from a primary care perspective covering three areas: diagnosis, posttraumatic stress disorder (PTSD), and end-of-life care. We analyzed patient records of every child diagnosed with a malignancy in a defined area. During 12 years 68 children were diagnosed (incidence 14/100,000). For 68% the diagnosis was initiated in primary care. There were 25 children with leukemia, and 22 with brain tumors. Median parent’s and doctor’s delay were 1 and 0 weeks for the former, and 5 and 3 weeks for the latter group. We found that diagnosis for 135 women with breast cancer, and 99 women with ovarian cancer was initiated in primary care for more than 50%. Median patient’s delay was 1 week for breast cancer, and 3.5 weeks for ovarian cancer patients, and provider delay 3 weeks for both groups. Crude and relative 5-year survival was 73% and 91% in breast cancer, and 40% and 49% in ovarian cancer. We found a possible PTSD prevalence of 6.5% (n=72) in 1113 primary care attenders. DSM-IV trauma criteria, and >35 for the Impact of Event Scale combined with >5 for the Posttraumatic Symptom Scale. Cancer was a triggering trauma for 20% of those with possible PTSD. Low well-being had the strongest association with possible PTSD followed by sexual assault, and female gender. We designed an attitude questionnaire to evaluate a learner-centered education in end-of-life care for home care staff. The Hospital Anxiety and Depression scale was used to measure well-being. Attitudes towards end-of-life care improved, and mental well-being increased in the intervention group, while no positive changes were seen in the control group. We did a grounded theory analysis and found that the basic process balancing explains the problem-solving in end-of-life cancer care. Four main balancing stages emerged. Weighing by sensing needs and wishes, and gauging against resources in diagnosing and care planning. Shifting by breaking bad news, changing careplaces and treatments. Compensating by controlling symptoms, educating, team-working, prioritizing and "stretching" time, innovating, improvising, and upholding the "homeostasis of hope". Compromising, the resulting stage, was a "walk on a fine line", between optimizing the care and deceiving the patient. Balancing was also used to conceptualize cancer care in general using data from all of the studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".